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Efficient invariant features for sensor variability compensation in speaker recognition.

Abdennour Alimohad1, Ahmed Bouridane2, Abderrezak Guessoum3

  • 1Research Laboratory in Electrical Engineering and Automatic LREA, University of MEDEA, Ain D'heb, Medea 26000, Algeria. alimohad@msn.com.

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|October 15, 2014
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Summary
This summary is machine-generated.

Invariant features enhance speaker recognition by addressing sensor variability. Combining them with mel frequency cepstral coefficients (MFCC) improves performance in uncontrolled conditions, reducing errors.

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Area of Science:

  • Speech processing
  • Biometrics
  • Machine learning

Background:

  • Speaker recognition systems often suffer performance degradation due to sensor variability.
  • Mel frequency cepstral coefficients (MFCC) are commonly used but can be sensitive to uncontrolled conditions.

Purpose of the Study:

  • To investigate the effectiveness of invariant features for speaker recognition.
  • To improve robustness against sensor variability and enhance recognition accuracy.

Main Methods:

  • Utilized invariant features designed to be robust against sensor variations.
  • Experimented with combining invariant features and MFCCs.
  • Evaluated performance using equal error rate (EER) and minimum decision cost function (minDCF).

Main Results:

  • Invariant features demonstrated effectiveness in controlled (match) conditions.
  • Combining invariant features with MFCCs significantly improved performance in uncontrolled (mismatch) conditions.
  • The proposed approach led to a reduction in EER and minDCF compared to traditional GMM-UBM systems using only MFCCs.

Conclusions:

  • Invariant features offer a promising solution for robust speaker recognition.
  • Hybrid approaches combining invariant features and MFCCs are beneficial for handling real-world acoustic variability.